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2024 60th annual allerton conference on communication, control, and computing, allerton 2024

2024 60th Annual Allerton Conference on Communication, Contr...

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60th annual allerton conference on communication, control, and computing, allerton 2024

ISBN: (纸本)9798331541033

the proceedings contain 71 papers. the topics discussed include: transferable learning of GCN sampling graph data clusters from different power systems;strategies for rate optimization in joint communication and sensing over channels with memory;distributed matrix multiplication: download rate, randomness and privacy trade-offs;Minkowski descent: an algorithm for stochastic global optimization;quickest change detection using mismatched CUSUM;a new framework for designing polynomial codes for private information retrieval;a hypothesis testing-based framework for cyber deception with sludging;generalized barrier functions: integral conditions & recurrent relaxations;and adaptive online model update algorithm for predictive control in networked systems.

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2023 59th annual allerton conference on communication, control, and computing, allerton 2023

2023 59th Annual Allerton Conference on Communication, Contr...

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59th annual allerton conference on communication, control, and computing, allerton 2023

ISBN: (纸本)9798350328141

the proceedings contain 85 papers. the topics discussed include: on energy-delay tradeoff in uncoordinated MAC;curse of dimension of thompson sampling for combinatorial bandits;dynamic load balancing of selfish drivers between spatially distributed electrical vehicle charging stations;theoretical analysis of binary masks in snapshot compressive imaging systems;learning source coding for general alphabets and finite state machines;straggler exploitation in distributed computing systems with task grouping;downlink beamforming optimization via deep learning;FedRec+: enhancing privacy and addressing heterogeneity in federated recommendation systems;stochastic control under correlated disturbances;and dynamic tolling in arc-based traffic assignment models.

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2022 58th annual allerton conference on communication, control, and computing, allerton 2022

2022 58th Annual Allerton Conference on Communication, Contr...

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58th annual allerton conference on communication, control, and computing, allerton 2022

ISBN: (纸本)9798350399981

the proceedings contain 89 papers. the topics discussed include: active learning for individual data via minimal stochastic complexity;byzantine resilience with reputation scores;over-the-air federated learning with privacy protection via correlated additive perturbations;bounds on reversible, complement, reversible-complement, constant weight sum codes;extracting unique information through Markov relations;quickest detection of a threat to an impending disaster;online detection of cascading change-points using diffusion networks;bandits with dynamic arm-acquisition costs;uncertainty in biometric identification and authentication systems with strong secrecy;finite-blocklength results for the a-channel: applications to unsourced random access and group testing;and repeated games, optimal channel capture, and open problems for slotted multiple access.

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Solving the B-SAT Problem Using Quantum computing: Smaller is Sometimes Better 60

Solving the B-SAT Problem Using Quantum Computing: Smaller i...

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60th annual allerton conference on communication, control, and computing, allerton 2024

作者： Bennakhi, Ahmad Byrd, Gregory T. Franzon, Paul North Carolina State University ECE Dept. Raleigh United States

ISBN: (纸本)9798331541033

this paper examines the effectiveness of modern universal gate quantum computers in solving the Boolean Satisfiability (B-SAT) problem using Grover's Search algorithm. Experiments were conducted with varying configurations, including the number of shots, qubit mapping, and different quantum processors, to assess their impact on the results. the study also includes an experiment that highlights a unique behavior observed in IBM quantum processors when running circuits with a specific number of shots. © 2024 IEEE.

关键词： Electronic design automation

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Linearly Solvable General-Sum Markov Games 60

Linearly Solvable General-Sum Markov Games

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60th annual allerton conference on communication, control, and computing, allerton 2024

作者： Soper, Braden C. Miller, Caleb J. Merl, Dan M. Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States

ISBN: (纸本)9798331541033

Linearly solvable Markov decision processes (LSMDPs) are a special class of Markov decision processes (MDPs) in which the optimal value function under an exponential transformation satisfies a linear equation. this model was previously extended to a class of two-player, zero-sum Markov games in which the game's equilibrium value function can similarly be derived from a linear equation. In this work, a new class of linearly solvable n-player, general-sum Markov games is proposed. We show that games in this class have a unique Nash equilibrium, and the equilibrium value functions can be derived from a single system of linear equations. We demonstrate how to approximate discrete-state, discrete-action Markov games outside this class through an embedding process analogous to the way LSMDPs approximate standard MDPs. An off-policy reinforcement learning algorithm for general-sum Markov games, which we call Nash-Z learning, is presented. We empirically demonstrate that Nash-Z learning finds equilibrium policies with low regret and that it finds these solutions orders of magnitude faster than the classic Nash-Q learning algorithm. © 2024 IEEE.

关键词： Nash equilibrium

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Lossless Binary Image Compression Using Learned Multi-Level Dictionaries 60

Lossless Binary Image Compression Using Learned Multi-Level ...

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60th annual allerton conference on communication, control, and computing, allerton 2024

作者： Agnihotri, Samar Rameshan, Renu M. Ghosal, Ritwik Gupta, Prerit School of Computing and EE IIT HP Mandi175005 India Vehant Technologies Pvt. Ltd. UP 201301 India Shiv Nadar University Department of CSE UP 201314 India

ISBN: (纸本)9798331541033

Lossless image compression is required in various applications to reduce storage or transmission costs of images, while requiring the reconstructed images to have zero information loss compared to the original. Existing lossless image compression methods either have simple design but poor compression performance, or complex design, better performance, but with no performance guarantees. In our endeavor to develop a lossless image compression method with low complexity and guaranteed performance, we argue that compressibility of a color image is essentially derived from the patterns in its spatial structure, intensity variations, and color variations. thus, we divide the overall design of a lossless image compression scheme into three parts that exploit corresponding redundancies. We further argue that the binarized version of an image captures its fundamental spatial structure. In this first part of our work, we propose a scheme for lossless compression of binary images. the proposed scheme first learns dictionaries of 16 × 16, 8 × 8, 4 × 4, and 2 × 2 square pixel patterns from various datasets of binary images. It then uses these dictionaries to encode binary images. these dictionaries have various interesting properties that are further exploited to construct an efficient and scalable scheme. Our preliminary results show that the proposed scheme consistently outperforms existing conventional and learning based lossless compression approaches, and provides, on average, as much as 1.5× better performance than a common lossless com-pression scheme (WebP), more than 3 × better performance than a state of the art learning based scheme, and better performance than a specialized scheme for binary image compression (JBIG2). © 2024 IEEE.

关键词： Binary images

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An Algorithm for computing the Capacity of Symmetrized KL Information for Discrete Channels 60

An Algorithm for Computing the Capacity of Symmetrized KL In...

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60th annual allerton conference on communication, control, and computing, allerton 2024

作者： Chen, Haobo Aminian, Gholamali Bu, Yuheng University of Florida Department of ECE Gainesville United States The Alan Turing Institute London United Kingdom

ISBN: (纸本)9798331541033

Symmetrized Kullback-Leibler (KL) information (ISKL), which symmetrizes the traditional mutual information by integrating Lautum information, has been shown as a critical quantity in communication [1] and learning theory [2]. this paper considers the problem of computing the capacity in terms of ISKL for a fixed discrete channel. Such a maximization problem is reformulated into a discrete quadratic optimization with a simplex constraint. One major challenge here is the non-concavity of Lautum information, which complicates the optimization problem. Our method involves symmetrizing the KL divergence matrix and applying iterative updates to ensure a non-decreasing update while maintaining a valid probability distribution. We validate our algorithm on Binary symmetric Channels and Binomial Channels, demonstrating its consistency with theoretical values. Additionally, we explore its application in machine learning through the Gibbs channel, showcasing the effectiveness of our algorithm in finding the worst-case data distributions. © 2024 IEEE.

关键词： channel capacity Gibbs channel quadratic optimization Symmetrized KL information

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Multi-Functional Distributed computing 60

Multi-Functional Distributed Computing

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60th annual allerton conference on communication, control, and computing, allerton 2024

作者： Malak, Derya Salehi, Mohammad Reza Deylam Serbetci, Berksan Elia, Petros EURECOM Biot Sophia Antipolis Communication Systems Department France

ISBN: (纸本)9798331541033

the work here studies the communication cost for a multi-server, multi-task distributed computation framework, and does so for a broad class of functions and data statistics. Considering the framework where a user seeks the computation of multiple complex (conceivably non-linear) tasks from a set of distributed servers, we establish communication cost upper bounds for a variety of data statistics, function classes and data placements across the servers. To do so, we proceed to apply, for the first time here, Korner's characteristic graph approach - which is known to capture the structural properties of data and functions - to the promising framework of multi-server multi-task distributed computing. Going beyond the general expressions, and in order to offer clearer insight, we also consider the well-known scenario of cyclic dataset placement and linearly separable functions over the binary field, in which case our approach exhibits considerable gains over the state of the art. Similar gains are identified for multilinear functions. © 2024 IEEE.

关键词： charac-teristic graph entropy data correlations Distributed computation functional compression linearly separable functions nonlinear functions skewed statistics

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Straggler Exploitation in Distributed computing Systems with Task Grouping 59

Straggler Exploitation in Distributed Computing Systems with...

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59th annual allerton conference on communication, control, and computing, allerton 2023

作者： Adikari, tharindu Al-Lawati, Haider Lam, Jason Hu, Zhenhua Draper, Stark University of Toronto Canada Huawei Technologies Canada Canada

ISBN: (纸本)9798350328141

We consider the problem of stragglers in distributed computing systems. Stragglers are compute nodes that unpredictably become slow. this often increases the completion times of tasks. One popular method for mitigating stragglers is to replicate work. Out of all replications of a task, only the first to complete is accepted. the rest are discarded. Discarding work completed by some of the workers leads to wastage of resources. In this paper, we propose a method for exploiting the work completed by stragglers rather than discarding it. We increase the granularity of the assigned work and the frequency of worker updates. We demonstrate that the proposed method reduces the completion time of tasks via experiments performed on a simulated cluster. © 2023 IEEE.

关键词： MapReduce

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Exploring Gradient Oscillation in Deep Neural Network Training 59

Exploring Gradient Oscillation in Deep Neural Network Traini...

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59th annual allerton conference on communication, control, and computing, allerton 2023

作者： Morchdi, Chedi Zhou, Yi Ding, Jie Wang, Bei University of Utah Department of Ece UT United States University of Minnesota School of Statistics MN United States University of Utah School of Computing UT United States

ISBN: (纸本)9798350328141

Understanding optimization in deep learning is a fundamental problem, and recent findings have challenged the previously held belief that gradient descent stably trains deep networks. In this study, we delve deeper into the instability of gradient descent during the training of deep networks. By employing gradient descent to train various modern deep networks, we provide empirical evidence demonstrating that a significant portion of the optimization progress occurs through the utilization of oscillating gradients. these gradients exhibit a high negative correlation between adjacent iterations. Furthermore, we make the following noteworthy observations about these gradient oscillations (GO): (i) GO manifests in different training stages for networks with diverse architectures;(ii) when using a large learning rate, GO consistently emerges across all layers of the networks;and (iii) when employing a small learning rate, GO is more prominent in the input layers compared to the output layers. these discoveries indicate that GO is an inherent characteristic of training different types of neural networks and may serve as a source of inspiration for the development of novel optimizer designs. © 2023 IEEE.

关键词： Optimization

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